Update README.md

README.md

@@ -24,6 +24,28 @@ All tests were performed on Intel(R) Xeon(R) CPU E5-2698 v4 @ 2.20GHz and nVIDIA
 
 To evaluate model quality, we fine-tuned DistilRuBERT-small on classification, NER and question answering tasks. Scores and archives with fine-tuned models can be found in [DeepPavlov docs](http://docs.deeppavlov.ai/en/master/features/overview.html#models).
 
+# Citation
+If you found the model useful for your research, we are kindly ask to cite [this](https://arxiv.org/abs/2205.02340) paper:
+```
+@misc{https://doi.org/10.48550/arxiv.2205.02340,
+  doi = {10.48550/ARXIV.2205.02340},
+  
+  url = {https://arxiv.org/abs/2205.02340},
+  
+  author = {Kolesnikova, Alina and Kuratov, Yuri and Konovalov, Vasily and Burtsev, Mikhail},
+  
+  keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
+  
+  title = {Knowledge Distillation of Russian Language Models with Reduction of Vocabulary},
+  
+  publisher = {arXiv},
+  
+  year = {2022},
+  
+  copyright = {arXiv.org perpetual, non-exclusive license}
+}
+```
+
 \[1\]: P. Lison and J. Tiedemann, 2016, OpenSubtitles2016: Extracting Large Parallel Corpora from Movie and TV Subtitles. In Proceedings of the 10th International Conference on Language Resources and Evaluation \(LREC 2016\)
 
 \[2\]: Shavrina T., Shapovalova O. \(2017\) TO THE METHODOLOGY OF CORPUS CONSTRUCTION FOR MACHINE LEARNING: «TAIGA» SYNTAX TREE CORPUS AND PARSER. in proc. of “CORPORA2017”, international conference , Saint-Petersbourg, 2017.